In [2]:
import imdlib as imd
import numpy as np
import matplotlib.pyplot as plt
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In [3]:
start_yr=1951
end_yr=1960
variable='tmax'
data=imd.get_data(variable, start_yr, end_yr, fn_format="yearwise")
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ds=data.get_xarray()
ds_time_series = ds.sel(lat=np.arange(18.75,19.25,0.25), lon=np.arange(72.50, 73.00,0.25),method= "nearest")
ds_time_series=ds_time_series['tmax'].mean(dim=('lat','lon'))
ds_time_series.plot(color="purple", aspect = 7, size = 8)
plt.savefig('Series_decade_1_tmax.png', bbox_inches='tight')
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start_yr=1961
end_yr=1970
variable='tmax'
data1=imd.get_data(variable, start_yr, end_yr, fn_format="yearwise")
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In [8]:
ds=data1.get_xarray()
ds_time_series = ds.sel(lat=np.arange(18.75,19.25,0.25), lon=np.arange(72.50, 73.00,0.25),method= "nearest")
ds_time_series=ds_time_series['tmax'].mean(dim=('lat','lon'))
ds_time_series.plot(color="purple", aspect = 7, size = 8)
plt.savefig('Series_decade_2_tmax.png', bbox_inches='tight')
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start_yr=1971
end_yr=1980
variable='tmax'
data2=imd.get_data(variable, start_yr, end_yr, fn_format="yearwise")
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ds=data2.get_xarray()
ds_time_series = ds.sel(lat=np.arange(18.75,19.25,0.25), lon=np.arange(72.50, 73.00,0.25),method= "nearest")
ds_time_series=ds_time_series['tmax'].mean(dim=('lat','lon'))
ds_time_series.plot(color="purple", aspect = 7, size = 8)
plt.savefig('Series_decade_3_tmax.png', bbox_inches='tight')
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start_yr=1981
end_yr=1990
variable='tmax'
data3=imd.get_data(variable, start_yr, end_yr, fn_format="yearwise")
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In [13]:
ds=data3.get_xarray()
ds_time_series = ds.sel(lat=np.arange(18.75,19.25,0.25), lon=np.arange(72.50, 73.00,0.25),method= "nearest")
ds_time_series=ds_time_series['tmax'].mean(dim=('lat','lon'))
ds_time_series.plot(color="purple", aspect = 7, size = 8)
plt.savefig('Series_decade_4_tmax.png', bbox_inches='tight')
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start_yr=1991
end_yr=2000
variable='tmax'
data4=imd.get_data(variable, start_yr, end_yr, fn_format="yearwise")
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In [16]:
ds=data4.get_xarray()
ds_time_series = ds.sel(lat=np.arange(18.75,19.25,0.25), lon=np.arange(72.50, 73.00,0.25),method= "nearest")
ds_time_series=ds_time_series['tmax'].mean(dim=('lat','lon'))
ds_time_series.plot(color="purple", aspect = 7, size = 8)
plt.savefig('Series_decade_5_tmax.png', bbox_inches='tight')
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start_yr=2001
end_yr=2010
variable='tmax'
data5=imd.get_data(variable, start_yr, end_yr, fn_format="yearwise")
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In [18]:
ds=data5.get_xarray()
ds_time_series = ds.sel(lat=np.arange(18.75,19.25,0.25), lon=np.arange(72.50, 73.00,0.25),method= "nearest")
ds_time_series=ds_time_series['tmax'].mean(dim=('lat','lon'))
ds_time_series.plot(color="purple", aspect = 7, size = 8)
plt.savefig('Series_decade_6_tmax.png', bbox_inches='tight')
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start_yr=2011
end_yr=2020
variable='tmax'
data6=imd.get_data(variable, start_yr, end_yr, fn_format="yearwise")
Downloading: maxtemp for year 2011
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In [20]:
ds=data6.get_xarray()
ds_time_series = ds.sel(lat=np.arange(18.75,19.25,0.25), lon=np.arange(72.50, 73.00,0.25),method= "nearest")
ds_time_series=ds_time_series['tmax'].mean(dim=('lat','lon'))
ds_time_series.plot(color="purple", aspect = 7, size = 8)
plt.savefig('Series_decade_7_tmax.png', bbox_inches='tight')
In [24]:
start_yr=1951
end_yr=2020
variable='tmax'
data_full=imd.open_data(variable, start_yr, end_yr, fn_format="yearwise")
In [31]:
ds=data_full.get_xarray()
ds_time_series = ds.sel(lat=np.arange(18.75,19.25,0.25), lon=np.arange(72.50, 73.00,0.25),method= "nearest")
ds_time_series=ds_time_series['tmax'].mean(dim=('lat','lon'))
ds_time_series.plot(color="purple", aspect = 5, size = 80)
Out[31]:
[<matplotlib.lines.Line2D at 0x20f607052e0>]
In [32]:
ds=data_full.get_xarray()
ds_time_series = ds.sel(lat=np.arange(18.75,19.25,0.25), lon=np.arange(72.50, 73.00,0.25),method= "nearest")
ds_time_series=ds_time_series['tmax'].mean(dim=('lat','lon'))
ds_time_series.plot(color="purple", aspect = 7, size = 8)
Out[32]:
[<matplotlib.lines.Line2D at 0x20f6067c610>]
In [33]:
#MINIMUM TEMPERATURES #####################################################################
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